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. 2023 Sep 20;7(6):389-400.
doi: 10.1093/evlett/qrad034. eCollection 2023 Dec.

Long-term evolution of antibiotic tolerance in Pseudomonas aeruginosa lung infections

Melanie Ghoul  1 , Sandra B Andersen  2 , Rasmus L Marvig  3 , Helle K Johansen  4   5 , Lars Jelsbak  6 , Søren Molin  5 , Gabriel Perron  7   8 , Ashleigh S Griffin  1
Affiliations

Long-term evolution of antibiotic tolerance in Pseudomonas aeruginosa lung infections

Melanie Ghoul et al. Evol Lett. .

Abstract

Pathogenic bacteria respond to antibiotic pressure with the evolution of resistance but survival can also depend on their ability to tolerate antibiotic treatment, known as tolerance. While a variety of resistance mechanisms and underlying genetics are well characterized in vitro and in vivo, an understanding of the evolution of tolerance, and how it interacts with resistance in situ is lacking. We assayed for tolerance and resistance in isolates of Pseudomonas aeruginosa from chronic cystic fibrosis lung infections spanning up to 40 years of evolution, with 3 clinically relevant antibiotics: meropenem, ciprofloxacin, and tobramycin. We present evidence that tolerance is under positive selection in the lung and that it can act as an evolutionary stepping stone to resistance. However, by examining evolutionary patterns across multiple patients in different clone types, a key result is that the potential for an association between the evolution of resistance and tolerance is not inevitable, and difficult to predict.

Keywords: adaptation; evolutionary medicine; microbial evolutionary genomics.

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Figures

Figure 1.
Figure 1.
Graphs show number of tolerant cells per isolate, measured as colony-forming units (CFUs), over time for the two different clone types and three different antibiotics, and the GLM fit using time to predict change in CFU. The dashed red line marks the cut-off of 75 CFU to classify isolates as either low or high tolerance. On the left, DK1, and on the right, DK2. Tolerance was measured as CFU counts at 10 times dilution of culture.
Figure 2.
Figure 2.
Graphs show change in resistance (minimum inhibitory concentration [MIC]) over time in the transmissible clone types DK1 (left side) and DK2 (right side), to three different antibiotics (GLM fits). Dashed red lines indicate clinical cut-off for classification of resistance (MIC of 4 for ciprofloxacin, and 8 for meropenem and tobramycin). Eight DK1 isolates with an MIC > 33 are not shown for meropenem).
Figure 3.
Figure 3.
(A) The phylogeny of clone type DK2 isolates with the tolerance (T) and resistance (R) phenotypes to ciprofloxacin (cipro, first two columns) and meropenem (mero, last two columns; red: presence, white: absence, strike-through: phenotype unknown). Branch lengths are not drawn to scale. Panels (B) and (C) show the modeled transition rates between phenotypic states, indicated by the stroke and number on arrows. We find that the dependent model fits the data best as resistance is unlikely to evolve before tolerance, from the wild-type susceptible and low tolerance phenotype. This is also reflected in (A), as resistance is only found in the absence of high tolerance once for ciprofloxacin and twice for meropenem. The dependent model for tobramycin was not significantly different from the null hypothesis of independent evolution of tolerance and resistance and is not shown.
Figure 4.
Figure 4.
(A) The phylogeny of clone type DK1 isolates with the tolerance (T) and resistance (R) phenotypes to meropenem (mero; red: presence, white: absence, strike-through: phenotype unknown). Branch lengths are not drawn to scale. Panel (B) shows the modeled transition rates between phenotypic states, indicated by the stroke and number on arrows. We find that the dependent model fits the data best as the high tolerance resistant phenotype is unlikely to evolve from the low tolerance resistant state. The dependent models for ciprofloxacin and tobramycin were not significantly different from the null hypothesis of independent evolution of tolerance and resistance and are not shown.
Figure 5.
Figure 5.
(A) The phylogeny of clone type DK1 isolates with the tolerance phenotypes (red: tolerance, white: low tolerance, strike-through: phenotype unknown) to the three antibiotics mapped. (B) For tolerance to ciprofloxacin and meropenem, the dependent model fits the data best as tolerance to ciprofloxacin is unlikely to evolve before tolerance to meropenem. This is also reflected in (A), as all isolates that are tolerant to ciprofloxacin also show tolerance to meropenem. Numbers and size of arrows indicate the modeled rate of transition, given the simplest model that assumes that all rates are equal. (C = ciprofloxacin, m = meropenem). (C) Similarly, for meropenem and tobramycin, the dependent model fits the data best as tolerance to tobramycin is unlikely to evolve before tolerance to meropenem. The dependent model for ciprofloxacin and tobramycin was not significantly different from the null hypothesis of independent evolution and is not shown.
Figure 6.
Figure 6.
Population density measured as max OD for tolerance-resistance categories. Boxplots show median max OD ±25 percentiles and values as dots. Grouping by –two-way ANOVA Tukey HSD test, p < .05 denoted by letters A–C, groups are significantly different if they do not share a letter (Supplementary Table S4). LS = low tolerance, susceptible; LR = low tolerance, resistant; HS = high tolerance, susceptible; HR = high tolerance, resistant.
Figure 7.
Figure 7.
Isolate sampling time for tolerance-resistance categories. Boxplots show median sampling time ±25 percentiles and values as dots. Grouping by –two-way ANOVA Tukey HSD test, p < .05 denoted by letters A–C (Supplementary Table S5). LS = low tolerance, susceptible; LR = low tolerance, resistant; HS = high tolerance, susceptible; HR = high tolerance, resistant.
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